A group of people or objects that share one or more common features.
Demography
Geography
Occupation
Time
Care requirements
Diagnosis
What is a sampling frame
Physical list
Ideally everyone or almost everyone in population
Used to draw your sample
Expensive, not always available.
Example: Master Address File.
What is a sample?
A sample is a subset of a population
Representativeness more important than size
Reasons for sampling
Expense
Time
Quality control
Two major types of samples
Random sample
Everyone has known non-zero probability
Non-random sample
Different selection probabilities
Some may have zero selection probability
Extreme example: World War II bombers
Image of bomber with indication of damage
Example: in school survey of drug use in teenagers
Who has lower selection probability?
Who has a zero selection probability?
Can you redefine your population?
Example: prisoner IQ study
Hypothetical study
Calculate average IQ of prisoners
Lower than general public
Conclude: criminals less intelligent than honest people(???)
Break #1
What you have learned
Random and non-random samples
What’s coming next
Different types of probability samples
Sampling
Sampling designs – Probability sampling
Simple random sampling
Systematic sampling
Stratified sampling
Cluster sampling
How to draw a simple random sample
List the sampling frame in a logical order
Attach a column of random numbers
Sort by the column of random numbers
Select your sample, starting at the top
Simple random sample using Microsoft Excel
A spreadsheet illustrating simple random sampling
How to draw a stratified random sample
List the sampling frame and strata in a logical order
Attach a column of random numbers
Sort by the strata and the column of random numbers
Select your sample, starting at the top
Stratified random sample using Microsoft Excel
A spreadsheet illustrating stratified random sampling
Live demo, ANOVA and R-squared
Break #2
What you have learned
Different types of probability samples
What’s coming next
Different types of non-probability samples
Break #2
What have we learned so far?
Types of probability samples
How to draw a random sample
What is coming up next?
Different types of non-probability samples
How to allocate treatments randomly
Sampling
Sampling designs – Nonprobability sampling
Convenience sampling
Quota sampling
Purposive sampling
Purposeful sampling
Snowball sampling
Example of a purposive sample
Table describing purposive sampling strategy
Randomizing treatments within a convenience sample
Many studies use a convenience sample, which may hamper external validity, but they randomly assign treatment or control conditions within the convenience sample, which helps with internal validity. The process works much like the process of drawing a simple random sample.
List your treatment groups in a logical order
Attach a column of random numbers
Sort by the column of random numbers
Allocate treatment groups, starting at the top of the list.
Randomizing treatment allocation using Microsoft Excel
A spreadsheet illustrating random treatment allocation
Randomizing a crossover trial using Microsoft Excel
A spreadsheet illustrating random allocation of treatment order
Live demo, partial F test
Break #3
What you have learned
Different types of non-probability samples
What’s coming next
Matching and pairing
Matching and pairing
Improved precision
Logistical issues
Works for both randomized and observational studies
The logistics of matching
Not obvious
Simplest solution: greedy matching
Unpaired patients are lost to your analysis
Extra precision from pairing
Loss of precision from loss of the unpaired.
The cross-over trial
Only for some randomized trials
Each subject serves as own control
Randomize treatment order
Beware of carry-over
Live demo, stepwise regression
Break #4
What you have learned
Matching and pairing
What’s coming next
The methods section
What purpose does a methods section serve?
Assessment of the quality of your research
Brag here about your rigor
Save limitations for discussion
Allow others to replicate/extend
Non-obvious details
What should not be included in the methods section
“The Methods section should include only information that was available at the time the plan or protocol for the study was being written; all information obtained during the study belongs in the Results section.”
Uniform requirements for manuscripts submitted to biomedical journals: Writing and editing for biomedical publication. J Pharmacol Pharmacother. 2010;1(1):42–58.
Exceptions
Patient counts, Dropout rates, Protocol changes
What belongs in the methods section
Every methods section is different
General structure
Participants
Materials
Procedures
Measures
Analysis
Participants
Where you will find your participants
Inclusion/exclusion criteria
Efforts to insure representativeness
Materials/Procedures
Only document the non-routine
Materials
Chemicals
Include company and location
Procedures
Running complex equipment
Multiple step laboratory methods
Measures
Outcome variables
Independent variables
Covariates
Validity/reliability
Analysis
Research hypotheses / questions
Sample size justification
Descriptive methods
Boilerplate: “Continuous variables were summarized as means and SDs, and categorical variables were summarized as percentages.” Saleem 2019.
Analysis
Statistical model
Adjustments for multiplicity
Handling missing values/dropout
Alpha level and one/two sided tests
Boilerplate: “All tests were two sided, and P values below the 5% level were regarded as significant.” Lokken 1995.
What goes in the methods section of a qualitative study